From 16cf76c429c31b1536175557a33858376537f574 Mon Sep 17 00:00:00 2001 From: brees Date: Wed, 4 Dec 2019 13:25:53 -0800 Subject: [PATCH] Added additional algorithms --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 0e7b07eee0b..eada5b834c4 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,8 @@ The [RAPIDS](https://rapids.ai) cuGraph library is a collection of graph analyti import cugraph # assuming that data has been loaded into a cuDF (using read_csv) Dataframe +gdf = cudf.read_csv("graph_data.csv", names=["src", "dst"], dtype=["int32", "int32"] ) + # create a Graph using the source (src) and destination (dst) vertex pairs the GDF G = cugraph.Graph() G.add_edge_list(gdf["src"], gdf["dst"]) @@ -32,12 +34,15 @@ for i in range(len(gdf_page)): | :-------------------------------------------- | ---------- | ---------------------------- | | PageRank | Multi-GPU | | | Personal PageRank | Single-GPU | | +| Katz Centrality | Single-GPU | | | Jaccard Similarity | Single-GPU | | | Weighted Jaccard | Single-GPU | | | Overlap Similarity | Single-GPU | | | SSSP | Single-GPU | Updated to provide path info | | BFS | Single-GPU | | | Triangle Counting | Single-GPU | | +| K-Core | Single-GPU | | +| Core Number | Single-GPU | | | Subgraph Extraction | Single-GPU | | | Spectral Clustering - Balanced-Cut | Single-GPU | | | Spectral Clustering - Modularity Maximization | Single-GPU | |